by Keyword: Data mining

Arcentales, A., Rivera, P., Caminal, P., Voss, A., Bayés-Genís, A., Giraldo, B. F., (2016). Analysis of blood pressure signal in patients with different ventricular ejection fraction using linear and non-linear methods Engineering in Medicine and Biology Society (EMBC) 38th Annual International Conference of the IEEE , IEEE (Orlando, USA) , 2700-2703

Changes in the left ventricle function produce alternans in the hemodynamic and electric behavior of the cardiovascular system. A total of 49 cardiomyopathy patients have been studied based on the blood pressure signal (BP), and were classified according to the left ventricular ejection fraction (LVEF) in low risk (LR: LVEF>35%, 17 patients) and high risk (HR: LVEF≤35, 32 patients) groups. We propose to characterize these patients using a linear and a nonlinear methods, based on the spectral estimation and the recurrence plot, respectively. From BP signal, we extracted each systolic time interval (STI), upward systolic slope (BPsl), and the difference between systolic and diastolic BP, defined as pulse pressure (PP). After, the best subset of parameters were obtained through the sequential feature selection (SFS) method. According to the results, the best classification was obtained using a combination of linear and nonlinear features from STI and PP parameters. For STI, the best combination was obtained considering the frequency peak and the diagonal structures of RP, with an area under the curve (AUC) of 79%. The same results were obtained when comparing PP values. Consequently, the use of combined linear and nonlinear parameters could improve the risk stratification of cardiomyopathy patients.

JTD Keywords: Feature extraction, Blood pressure, Heart rate, Estimation, Data mining, Covariance matrices, Hospitals